CN113408425B - Cluster control method and system for biological language analysis - Google Patents
Cluster control method and system for biological language analysis Download PDFInfo
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Abstract
The invention discloses a cluster control method and a system for biological language analysis, wherein the method comprises the following steps: obtaining first language signal information of a first biological category; performing feature extraction on the first language signal information to obtain a first biological language feature set; coding the first biological language feature set to obtain a first feature coding data set; obtaining a first cluster signal of a first machine; performing signal matching on the first feature encoding data set and the first cluster signal to obtain first signal feature matching data; constructing a first signal decoding model by taking the first signal feature matching data as training data; and sending the first signal decoding model to a first cluster where the first machine is located. The technical problem that radio wave interference has great influence on the efficiency and accuracy of signal transmission inside an unmanned aerial vehicle cluster in the prior art is solved.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle clusters, in particular to a cluster control method and system for biological language analysis.
Background
The unmanned aerial vehicle cluster mainly depends on an advanced and open communication network, the unmanned aerial vehicles have cooperative interaction capacity, the whole system presents group intelligence, and a single node has substitutability. By adopting the unmanned aerial vehicle clustering technology, the task can be completed quickly and effectively, and meanwhile, the whole system has the advantages of strong survivability, function distribution and the like. Although drone swarm networking communication has great potential for development, there are some key challenging issues. During the communication operation of the unmanned aerial vehicle cluster network, the data transmission quantity is increased sharply, the static spectrum allocation efficiency is not high, the performance of the cluster system is reduced, and the influence of various radio wave interference devices on radio signals also influences the transmission of signals in the unmanned aerial vehicle cluster.
In the process of implementing the technical scheme of the invention in the embodiment of the present application, the inventor of the present application finds that the above-mentioned technology has at least the following technical problems:
the interference of radio waves influences the efficiency and accuracy of signal transmission inside the unmanned aerial vehicle cluster, and the cluster control performance is reduced.
Disclosure of Invention
The embodiment of the application provides a cluster control method and system for biological language analysis, solves the technical problems that the interference of radio waves influences the efficiency and accuracy of signal transmission inside an unmanned aerial vehicle cluster in the prior art, and the cluster control performance is reduced, and realizes the technical purpose that signal transmission is carried out by extracting biological language features and simulating biological languages, so that the unmanned aerial vehicle cluster is free from radio interference, and the signal frequency band can carry out high-frequency transmission.
In view of the foregoing problems, embodiments of the present application provide a cluster control method and system for biological language analysis.
The application provides a cluster control method for biological language analysis, wherein the method comprises the following steps: obtaining first language signal information of a first biological category; performing feature extraction on the first language signal information to obtain a first biological language feature set; coding the first biological language feature set to obtain a first feature coding data set; obtaining a first cluster signal of a first machine; performing signal matching on the first feature encoding data set and the first cluster signal to obtain first signal feature matching data; constructing a first signal decoding model by taking the first signal feature matching data as training data; and sending the first signal decoding model to a first cluster where the first machine is located.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the method comprises the steps of obtaining first language signal information of a first biological class; performing feature extraction on the first language signal information to obtain a first biological language feature set; coding the first biological language feature set to obtain a first feature coding data set; obtaining a first cluster signal of a first machine; performing signal matching on the first feature encoding data set and the first cluster signal to obtain first signal feature matching data; constructing a first signal decoding model by taking the first signal feature matching data as training data; and sending the first signal decoding model to a first cluster where the first machine is located. The technical purpose that the signal transmission is carried out by extracting the biological language features and simulating the biological language is achieved, so that the unmanned aerial vehicle cluster is free from radio interference, and the signal frequency band can carry out high-frequency transmission.
The foregoing is a summary of the present disclosure, and embodiments of the present disclosure are described below to make the technical means of the present disclosure more clearly understood.
Drawings
Fig. 1 is a schematic flowchart of a cluster control method for biological language analysis according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of obtaining the first language signal information in a cluster control method for biological language analysis according to an embodiment of the present application;
fig. 3 is a schematic flowchart illustrating a process of obtaining a first biological language feature set in a cluster control method for biological language parsing according to an embodiment of the present application;
fig. 4 is a schematic flowchart illustrating a signal transmission process performed by a cluster in a cluster control method for biological language analysis according to an embodiment of the present application;
FIG. 5 is a schematic view illustrating a process of performing feature data dimension reduction in a cluster control method for biologics language analysis according to an embodiment of the present application;
fig. 6 is a schematic flowchart of incremental learning of the first signal decoding model in a cluster control method for biological language analysis according to an embodiment of the present application;
fig. 7 is a schematic flowchart illustrating a data training process performed by the first signal decoding model in a cluster control method for biological language analysis according to an embodiment of the present application;
FIG. 8 is a schematic structural diagram of a cluster control system for bio-language parsing according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a first sending unit 17, an electronic device 300, a memory 301, a processor 302, a communication interface 303, and a bus architecture 304.
Detailed Description
The embodiment of the application provides a cluster control method and system for biological language analysis, solves the technical problems that the interference of radio waves influences the efficiency and accuracy of signal transmission inside an unmanned aerial vehicle cluster in the prior art, and the cluster control performance is reduced, and realizes the technical purpose that signal transmission is carried out by extracting biological language features and simulating biological languages, so that the unmanned aerial vehicle cluster is free from radio interference, and the signal frequency band can carry out high-frequency transmission.
Hereinafter, example embodiments of the present application will be described in detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and it is to be understood that the present application is not limited by the example embodiments described herein.
Summary of the application
The unmanned aerial vehicle cluster mainly depends on an advanced and open communication network, the unmanned aerial vehicles have cooperative interaction capacity, the whole system presents group intelligence, and a single node has substitutability. By adopting the unmanned aerial vehicle clustering technology, the task can be completed quickly and effectively, and meanwhile, the whole system has the advantages of strong survivability, function distribution and the like. Although drone swarm networking communication has great potential for development, there are some key challenging issues. During the communication operation of the unmanned aerial vehicle cluster network, the data transmission quantity is increased sharply, the static spectrum allocation efficiency is not high, the performance of the cluster system is reduced, and the influence of various radio wave interference devices on radio signals also influences the transmission of signals in the unmanned aerial vehicle cluster. The technical problems that the interference of radio waves influences the efficiency and accuracy of signal transmission inside an unmanned aerial vehicle cluster and the cluster control performance is reduced exist in the prior art.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the present application further provides a cluster control system for bio-language parsing, wherein the system comprises: a first obtaining unit, configured to obtain first language signal information of a first biological category; the second obtaining unit is used for carrying out feature extraction on the first language signal information to obtain a first biological language feature set; a third obtaining unit, configured to perform encoding processing on the first biological language feature set to obtain a first feature encoding data set; a fourth obtaining unit for obtaining a first cluster signal of the first machine; a fifth obtaining unit, configured to perform signal matching on the first feature encoding data set and the first cluster signal to obtain first signal feature matching data; a sixth obtaining unit, configured to construct a first signal decoding model by using the first signal feature matching data as training data; a first sending unit, configured to send the first signal decoding model to a first cluster where the first machine is located.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, an embodiment of the present application provides a cluster control method for biological language parsing, where the method includes:
step S100: obtaining first language signal information of a first biological category;
specifically, radio interference refers to the phenomenon that some electromagnetic energy directly or indirectly enters a receiving system or a channel in a radio communication process, so that the quality of a useful receiving signal is reduced, information is generated and lost, and even communication is blocked. When unmanned aerial vehicle cluster operation, the radio wave interference device can reduce cluster system control performance to radio signal transmission's influence, and the information transfer of biological language then can not receive radio wave's influence, can realize stable, efficient signal transmission. Therefore, the characteristics of the biological language are analyzed and imitated, so that the unmanned aerial vehicle can transmit signals in the biological language mode. The first biological category is fish and birds, and in order to convert the original analog voice signal into a digital signal, two steps of sampling and quantizing are required. And acquiring and quantizing biological language information to obtain the first language signal information, wherein the first language signal information is a digital voice signal discrete in time and amplitude.
Further, as shown in fig. 2, step S100 in the embodiment of the present application further includes:
step S110: obtaining first language information of the first biological category;
step S120: preprocessing the first language information according to a first signal preprocessing mode to obtain second language information;
step S130: acquiring a first signal acquisition frequency;
step S140: acquiring signals of the second language information according to the first signal acquisition frequency to obtain first signal acquisition information;
step S150: obtaining a first quantization processing instruction;
step S160: and carrying out quantization processing on the first signal acquisition information according to the first quantization processing instruction to obtain the first language signal information.
Specifically, the first language information is an original language of the first biological category, and when sampling an original language signal, the speech signal is first preprocessed, where the first signal preprocessing method is filtering. The pre-filtering aims at two purposes, namely, aliasing interference is prevented; and secondly, power frequency interference of a power supply is restrained. And after the preprocessing is finished, sampling the second language information according to the first signal acquisition frequency so as to realize the discretization of the signal in time. The first signal acquisition frequency needs to be determined in combination with factors that the sampling frequency must be greater than twice the highest frequency of the signal under test. After the original signal is sampled, the amplitude is discretized through quantization processing, and therefore a digital voice signal which is discrete in time and amplitude is obtained.
Step S200: performing feature extraction on the first language signal information to obtain a first biological language feature set;
specifically, after the biological language information is processed, a digital signal, i.e., the first language signal information, which is characterized by being divided into time domain and frequency domain aspects, is obtained. And framing the sound domain by using the short-time stationarity of sound, and respectively extracting the features of each frame, thereby obtaining the feature set of the first language signal information, namely the first biological language feature set. The acquisition of the first biological language feature set realizes the decryption of the language characteristics of the biological language and lays a foundation for signal transmission by using the biological language.
Further, as shown in fig. 3, step S200 in the embodiment of the present application further includes:
step S210: obtaining first frame length information;
step S220: performing framing processing on the first language signal information according to the first frame length information to obtain an N-frame language signal information set;
step S230: respectively extracting the characteristics of the N frames of language signal information sets to obtain first characteristic parameter information;
step S240: and obtaining the first biological language feature set according to the first feature parameter information.
In particular, although the speech signal has a time-varying characteristic, its characteristic remains substantially unchanged, i.e., relatively stable, over a short time span, and thus it can be considered as a quasi-stationary process, i.e., the speech signal has a short-time stationarity. Therefore, the analysis processing of the first language signal information needs to be established on the basis of "short time", that is, "short time analysis" is performed, and the speech signal is subjected to frame division processing according to the first frame length information, so that the language signal information of each frame is subjected to feature extraction respectively. The first frame length information is determined according to the language characteristic of the first biological category. And after performing feature analysis on the first language signal information through frame division processing and extracting feature parameters of the first language signal information, obtaining the first biological language feature set, wherein the first biological language feature set is a feature parameter sequence formed by the feature parameters of each frame. And a foundation is laid for cracking and coding the biological language by determining the first biological language feature set.
Step S300: coding the first biological language feature set to obtain a first feature coding data set;
specifically, after the first biological language feature set is obtained, the first biological language feature set is subjected to coding processing, so that the identification codes of the features of the biological languages are obtained, and therefore after a language signal is obtained, the purpose of the language identification signal can be achieved by performing feature analysis on the signal and performing decoding operation by combining language paraphrases corresponding to the features. The decoding process is a process of seeking the optimal path for language identification. The first feature encoding data set is an identification code corresponding to each language feature, and is a basis for carrying out language identification on the language signal.
Step S400: obtaining a first cluster signal of a first machine;
step S500: performing signal matching on the first feature encoding data set and the first cluster signal to obtain first signal feature matching data;
specifically, the first cluster signal is a task instruction signal sent by the interior of the cluster through a task instruction when the unmanned aerial vehicle cluster performs functions of real-time tracking and positioning, remote control and remote measurement, real-time task planning and coordination, task information transmission and the like when executing a task. Different signals represent different operation instructions, so that the first characteristic encoding data set is subjected to signal matching with the first cluster signal, namely, each execution signal is correspondingly matched with the characteristic code in the first characteristic encoding data set, mapping between the execution signal and the characteristic code is realized, thereby realizing the certainty of the signal and the instruction when signal transmission is carried out based on biological language, and improving the accuracy of language identification and instruction receiving.
Step S600: constructing a first signal decoding model by taking the first signal feature matching data as training data;
step S700: and sending the first signal decoding model to a first cluster where the first machine is located.
Specifically, the first signal feature matching data includes a mapping relation between an instruction signal and a language feature code, so that the first signal feature matching data is used as training data to construct the first signal decoding model, the first signal decoding model is a neural network model, the first signal decoding model has the characteristic of continuously learning and acquiring experience to process data, and the received language signal can be converted into an execution instruction through deep learning, and required execution instruction information is converted into language information to be sent and transmitted. Therefore, signal transmission in the unmanned aerial vehicle cluster by simulating the biological language characteristic is realized, and the technical purpose of avoiding radio wave interference on signal transmission is achieved. And sending the first signal decoding model to a first cluster where the first machine is located, so as to realize signal processing through the first signal decoding model in the cluster.
Further, as shown in fig. 4, step S700 in the embodiment of the present application further includes:
step S710: obtaining first control instruction information;
step S720: obtaining a first information receiving instruction;
step S730: the first machine receives an instruction according to the first information, obtains first control instruction information, inputs the first control instruction information into the first signal decoding model, and obtains second language signal information;
step S740: obtaining a first signal sending instruction;
step S750: and the first machine sends the second language signal information to a second machine according to the first signal sending instruction.
Specifically, the first machine is a "head unit" inside the drone cluster and is responsible for sending command signals. Therefore, the first machine receives an instruction according to the first information, obtains the first control instruction information preset in the machine, and obtains second language signal information by inputting the first control instruction information into the first signal decoding model, wherein the second language signal information is a language signal simulating biological language for signal transmission, and the first signal decoding model is used for decoding and converting the signal to convert the control instruction into a biological language signal, namely the second language signal information. And then the first machine transmits the first language signal information to the second machine according to the transmission of the second language signal information dead center signal, wherein the second machine is a secondary head machine in the unmanned aerial vehicle cluster and is responsible for receiving the signal transmitted by the head machine, and transmits and executes the received control instruction through the first signal decoding model.
Further, as shown in fig. 5, step S600 in the embodiment of the present application further includes:
step S610: obtaining a first feature data set from the first signal feature matching data;
step S620: performing centralized processing on the first characteristic data set to obtain a second characteristic data set;
step S630: obtaining a first covariance matrix of the second feature data set;
step S640: calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
step S650: and projecting the first feature data set to the first feature vector to obtain a first dimension reduction data set, wherein the first dimension reduction data set is the feature data set obtained after dimension reduction of the first feature data set.
In particular, the first feature data set is a first feature data set obtained from the first signal feature matching data. And carrying out numerical processing on the feature data extracted from the first feature database, and constructing a feature data set matrix to obtain the first feature data set. And then carrying out centralization processing on each feature data in the first feature data set, firstly solving an average value of each feature in the first feature data set, then subtracting the average value of each feature from each feature for all samples, and then obtaining a new feature value, wherein the second feature data set is formed by the new feature data set, and is a data matrix. By the covariance formula:
and operating the second characteristic data set to obtain the first covariance matrix of the second characteristic data set. Wherein x _1^ i is the feature data in the second feature data set; x is the number of-Is the average value of the characteristic data; and M is the total amount of sample data in the second characteristic data set. Then, through matrix operation, the eigenvalue and the eigenvector of the first covariance matrix are solved, and each eigenvalue corresponds to one eigenvector. And selecting the largest first K characteristic values and the corresponding characteristic vectors from the obtained first characteristic vectors, and projecting the original characteristics in the first characteristic data set onto the selected characteristic vectors to obtain the first characteristic data set after dimension reduction. The K characteristic values can be obtained through training of the neural network model, and the accuracy of the data volume is guaranteed through the value of the K value. The feature data in the database are subjected to dimensionality reduction processing through a principal component analysis method, and redundant data are removed on the premise of ensuring the information quantity, so that the sample quantity of the feature data in the database is reduced, the loss of the information quantity after dimensionality reduction is minimum, and the operation speed of a training model on the data is accelerated.
Further, as shown in fig. 6, step S650 in this embodiment of the present application further includes:
step S651: inputting the first control instruction information to the first signal decoding model by taking the first dimension reduction data set as training data to obtain third language signal information;
step S652: analyzing the second language signal information and the third language signal information to obtain first defect data;
step S653: and inputting the first defect data into the first signal decoding model for incremental learning to obtain a second signal decoding model.
Specifically, after the dimension reduction processing is performed on the first signal feature matching data, the first dimension reduction dataset is obtained, and the first dimension reduction dataset has a characteristic that the sample size is more accurate, so that the first dimension reduction dataset is used as training data, and the first control instruction information is input to the first signal decoding model to obtain third language signal information. And then, performing defect data analysis on the second language signal information and the third language signal information to obtain first defect data, inputting the first defect data to the first signal decoding model for incremental learning, obtaining the second signal decoding model through the incremental learning, and improving the accuracy of the model and the response efficiency of the model through the incremental learning.
Further, as shown in fig. 7, step S730 in the embodiment of the present application further includes:
step S731: inputting the first control instruction information to the first signal decoding model, where the first signal decoding model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the first control instruction information and identification information for identifying the second language signal information;
step S732: and obtaining a first output result of the first signal decoding model, wherein the first output result is the second language signal information.
Specifically, the first signal decoding model is a neural network model obtained by training a plurality of sets of training data, and the process of training the neural network model by the training data is essentially a supervised learning process. Each of the training data in the plurality of sets includes the first control instruction information and identification information for identifying the second language signal information; building a plurality of groups of training data by using the first control instruction information and identification information for identifying the second language signal information, wherein under the condition of obtaining the first control instruction information, the neural network model outputs the identification information of the second language signal information to check the second language signal information output by the neural network model, and if the output second language signal information is consistent with the identified second language signal information, the data supervised learning is finished, and then the next group of data supervised learning is carried out; and if the output second language signal information is inconsistent with the identified second language signal information, adjusting the neural network model by the neural network model, and performing supervised learning of the next group of data after the neural network model reaches the expected accuracy. The neural network model is continuously corrected and optimized through training data, the accuracy of the neural network model for processing the data is improved through a supervised learning process, and the second language signal information is more accurate.
To sum up, the cluster control method for biological language parsing provided by the embodiment of the present application has the following technical effects:
1. the method comprises the steps of obtaining first language signal information of a first biological class; performing feature extraction on the first language signal information to obtain a first biological language feature set; coding the first biological language feature set to obtain a first feature coding data set; obtaining a first cluster signal of a first machine; performing signal matching on the first feature encoding data set and the first cluster signal to obtain first signal feature matching data; constructing a first signal decoding model by taking the first signal feature matching data as training data; and sending the first signal decoding model to a first cluster where the first machine is located. The technical purpose that the signal transmission is carried out by extracting the biological language features and simulating the biological language is achieved, so that the unmanned aerial vehicle cluster is free from radio interference, and the signal frequency band can carry out high-frequency transmission.
2. The second language signal information is acquired by training and learning through the neural network model and inputting the first control instruction information into the first signal decoding model, and the acquisition of the second language signal information is more accurate through the training data based on the characteristic that the neural network model can continuously learn and acquire experience to process data, so that the cluster control system can perform signal transmission more accurately and efficiently.
3. Because the feature data in the first signal feature matching data are subjected to dimensionality reduction by a principal component analysis method, redundant data are removed on the premise of ensuring the information quantity, so that the sample quantity of the feature data in the database is reduced, the loss of the information quantity after dimensionality reduction is minimum, and the data operation speed of a training model is accelerated. And the model is optimized through incremental learning, and the technical effect of ensuring the stability and the accuracy of the output performance of the model is achieved.
Example two
Based on the same inventive concept as the cluster control method for biological language analysis in the foregoing embodiment, the present invention further provides a cluster control system for biological language analysis, as shown in fig. 8, where the system includes:
a first obtaining unit 11, wherein the first obtaining unit 11 is used for obtaining first language signal information of a first biological category;
a second obtaining unit 12, where the second obtaining unit 12 is configured to perform feature extraction on the first language signal information to obtain a first biological language feature set;
a third obtaining unit 13, where the third obtaining unit 13 is configured to perform encoding processing on the first biological language feature set to obtain a first feature encoding data set;
a fourth obtaining unit 14, the fourth obtaining unit 14 being configured to obtain a first cluster signal of the first machine;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to perform signal matching on the first feature encoding data set and the first cluster signal to obtain first signal feature matching data;
a sixth obtaining unit 16, where the sixth obtaining unit 16 is configured to use the first signal feature matching data as training data to construct a first signal decoding model;
a first sending unit 17, where the first sending unit 17 is configured to send the first signal decoding model to a first cluster where the first machine is located.
Further, the system further comprises:
a seventh obtaining unit configured to obtain first language information of the first biological category;
an eighth obtaining unit, configured to pre-process the first language information according to a first signal pre-processing manner, so as to obtain second language information;
a ninth obtaining unit, configured to obtain a first signal acquisition frequency;
a tenth obtaining unit, configured to perform signal acquisition on the second language information according to the first signal acquisition frequency to obtain first signal acquisition information;
an eleventh obtaining unit configured to obtain a first quantization processing instruction;
a twelfth obtaining unit, configured to perform quantization processing on the first signal acquisition information according to the first quantization processing instruction, so as to obtain the first language signal information.
Further, the system further comprises:
a thirteenth obtaining unit configured to obtain first frame length information;
a fourteenth obtaining unit, configured to perform framing processing on the first language signal information according to the first frame length information, so as to obtain an N-frame language signal information set;
a fifteenth obtaining unit, configured to perform feature extraction on the N-frame speech signal information sets respectively to obtain first feature parameter information;
a sixteenth obtaining unit, configured to obtain the first set of biological language features according to the first feature parameter information.
Further, the system further comprises:
a seventeenth obtaining unit configured to obtain first control instruction information;
an eighteenth obtaining unit configured to obtain a first information receiving instruction;
a nineteenth obtaining unit, configured to receive, by the first machine, an instruction according to the first information, obtain the first control instruction information, input the first control instruction information to the first signal decoding model, and obtain second language signal information;
a twentieth obtaining unit configured to obtain a first signal transmission instruction;
a twenty-first obtaining unit, configured to send, by the first machine according to the first signal sending instruction, the second language signal information to a second machine.
Further, the system further comprises:
a twenty-second obtaining unit for obtaining a first feature data set from the first signal feature matching data;
a twenty-third obtaining unit, configured to perform centralized processing on the first feature data set to obtain a second feature data set;
a twenty-fourth obtaining unit for obtaining a first covariance matrix of the second feature data set;
a twenty-fifth obtaining unit, configured to perform operation on the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
a twenty-sixth obtaining unit, configured to project the first feature data set to the first feature vector to obtain a first dimension reduction data set, where the first dimension reduction data set is a feature data set obtained after dimension reduction of the first feature data set.
Further, the system further comprises:
a first input unit, configured to input the first control instruction information to the first signal decoding model by using the first dimension reduction data set as training data, so as to obtain third language signal information;
a twenty-seventh obtaining unit, configured to perform defect data analysis on the second speech signal information and the third speech signal information, and obtain first defect data;
and the second input unit is used for inputting the first defect data into the first signal decoding model for incremental learning to obtain a second signal decoding model.
Further, the system further comprises:
a third input unit, configured to input the first control instruction information to the first signal decoding model, where the first signal decoding model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the first control instruction information and identification information for identifying the second language signal information;
a twenty-eighth obtaining unit, configured to obtain a first output result of the first signal decoding model, where the first output result is the second language signal information.
The foregoing bio-language analysis cluster control method and the specific example in the first embodiment of fig. 1 are also applicable to the bio-language analysis cluster control system of the present embodiment, and a person skilled in the art can clearly know the bio-language analysis cluster control system of the present embodiment through the foregoing detailed description of the bio-language analysis cluster control method, so for the brevity of the description, detailed descriptions are omitted here.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to figure 9,
based on the same inventive concept as the cluster control method for biological language analysis in the foregoing embodiments, an embodiment of the present application further provides a cluster control system for biological language analysis, including: a processor coupled to a memory for storing a program that, when executed by the processor, causes a system to perform the method of any of the first aspects
The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 3, but this does not mean only one bus or one type of bus.
The communication interface 303 may be any device, such as a transceiver, for communicating with other devices or communication networks, such as an ethernet, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), a wired access network, and the like.
The memory 301 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an electrically erasable Programmable read-only memory (EEPROM), a compact disk read-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.
The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is configured to execute the computer executable instructions stored in the memory 301, so as to implement a cluster control method for biological language parsing provided by the above-mentioned embodiments of the present application.
Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.
The application provides a cluster control method for biological language analysis, wherein the method comprises the following steps: obtaining first surgical information; constructing a first surgical anesthesia material knowledge base; inputting the first surgical information into the first surgical anesthesia material knowledge base to obtain a first anesthesia material list; acquiring first anesthetic material management image information; inputting the first narcotic material management image information into a first characteristic identification model to obtain first category information of each narcotic material in the first narcotic material management image information; judging whether the first type information is matched with the first narcotic material list or not; if the first type information is matched with the first narcotic material list, first characteristic identification information of each narcotic material is obtained; acquiring a first usage dynamic state of each anesthetic material according to the first characteristic identification information; a first narcotic asset usage report of the first surgical information is dynamically generated as a function of the first usage. The technical problem of among the prior art rely on the manual work to the outfit of anesthesia goods and materials among the operation process and the statistics of using the dynamic condition, lead to the lower technical problem of goods and materials management efficiency, realized carrying out intelligent proofreading and real time monitoring apparatus and using the developments to operating room anesthesia apparatus through artificial intelligence, realized the technical purpose to the high efficiency management of anesthesia goods and materials.
Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer finger
The instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, where the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in this application may be implemented or operated upon by design of a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside in different components within the terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations.
Claims (8)
1. A cluster control method of bio-language parsing, wherein the method comprises:
obtaining first language signal information of a first biological category;
performing feature extraction on the first language signal information to obtain a first biological language feature set;
coding the first biological language feature set to obtain a first feature coding data set;
obtaining a first cluster signal of a first machine;
performing signal matching on the first feature encoding data set and the first cluster signal to obtain first signal feature matching data;
constructing a first signal decoding model by taking the first signal feature matching data as training data;
sending the first signal decoding model to a first cluster where the first machine is located;
after the sending the first signal decoding model to the first cluster where the first machine is located, the method further includes:
obtaining first control instruction information;
obtaining a first information receiving instruction;
the first machine receives an instruction according to the first information, obtains first control instruction information, inputs the first control instruction information into the first signal decoding model, and obtains second language signal information;
obtaining a first signal sending instruction;
and the first machine sends the second language signal information to a second machine according to the first signal sending instruction.
2. The method of claim 1, wherein the method comprises:
obtaining first language information of the first biological category;
preprocessing the first language information according to a first signal preprocessing mode to obtain second language information;
acquiring a first signal acquisition frequency;
acquiring signals of the second language information according to the first signal acquisition frequency to obtain first signal acquisition information;
obtaining a first quantization processing instruction;
and carrying out quantization processing on the first signal acquisition information according to the first quantization processing instruction to obtain the first language signal information.
3. The method of claim 1, wherein the obtaining a first set of biological language features further comprises:
obtaining first frame length information;
performing framing processing on the first language signal information according to the first frame length information to obtain an N-frame language signal information set;
respectively extracting the characteristics of the N frames of language signal information sets to obtain first characteristic parameter information;
and obtaining the first biological language feature set according to the first feature parameter information.
4. The method of claim 1, wherein the method further comprises:
obtaining a first feature data set from the first signal feature matching data;
performing centralized processing on the first characteristic data set to obtain a second characteristic data set;
obtaining a first covariance matrix of the second feature data set;
calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
and projecting the first feature data set to the first feature vector to obtain a first dimension reduction data set, wherein the first dimension reduction data set is the feature data set obtained after dimension reduction of the first feature data set.
5. The method of claim 4, wherein the method comprises:
inputting first control instruction information to the first signal decoding model by taking the first dimension reduction data set as training data to obtain third language signal information;
analyzing the defect data of the second language signal information and the third language signal information to obtain first defect data;
and inputting the first defect data into the first signal decoding model for incremental learning to obtain a second signal decoding model.
6. The method of claim 1, wherein the method comprises:
inputting the first control instruction information to the first signal decoding model, where the first signal decoding model is obtained by training multiple sets of training data, and each set of training data in the multiple sets includes: the first control instruction information and identification information for identifying the second language signal information;
and obtaining a first output result of the first signal decoding model, wherein the first output result is the second language signal information.
7. A cluster control system for bio-language parsing, wherein the system comprises:
a first obtaining unit, configured to obtain first language signal information of a first biological category;
the second obtaining unit is used for carrying out feature extraction on the first language signal information to obtain a first biological language feature set;
a third obtaining unit, configured to perform encoding processing on the first biological language feature set to obtain a first feature encoding data set;
a fourth obtaining unit for obtaining a first cluster signal of the first machine;
a fifth obtaining unit, configured to perform signal matching on the first feature encoding data set and the first cluster signal to obtain first signal feature matching data;
a sixth obtaining unit, configured to construct a first signal decoding model by using the first signal feature matching data as training data;
the first sending unit is used for sending the first signal decoding model to a first cluster where the first machine is located and then obtaining first control instruction information;
obtaining a first information receiving instruction;
the first machine receives an instruction according to the first information, obtains first control instruction information, inputs the first control instruction information into the first signal decoding model, and obtains second language signal information;
obtaining a first signal sending instruction;
and the first machine sends the second language signal information to a second machine according to the first signal sending instruction.
8. A cluster control system for bio-language parsing, comprising: a processor coupled with a memory, the memory for storing a program that, when executed by the processor, causes a system to perform the method of any of claims 1 to 6.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104885151A (en) * | 2012-12-21 | 2015-09-02 | 杜比实验室特许公司 | Object clustering for rendering object-based audio content based on perceptual criteria |
| WO2016011433A2 (en) * | 2014-07-17 | 2016-01-21 | Origin Wireless, Inc. | Wireless positioning systems |
| KR20190037025A (en) * | 2017-09-28 | 2019-04-05 | 서울대학교산학협력단 | Method for extracting signal unit features using variational inference model based deep learning and system thereof |
| EP3739356A1 (en) * | 2019-05-12 | 2020-11-18 | Origin Wireless, Inc. | Method, apparatus, and system for wireless tracking, scanning and monitoring |
| CN112348006A (en) * | 2021-01-11 | 2021-02-09 | 湖南星空机器人技术有限公司 | Unmanned aerial vehicle signal identification method, system, medium and equipment |
| CN112633415A (en) * | 2021-01-11 | 2021-04-09 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle cluster intelligent task execution method and device based on rule constraint training |
| CN112668652A (en) * | 2020-12-31 | 2021-04-16 | 哈尔滨工业大学 | Method and system for identifying cluster array and motion trend in unmanned equipment confrontation |
-
2021
- 2021-06-21 CN CN202110688353.5A patent/CN113408425B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104885151A (en) * | 2012-12-21 | 2015-09-02 | 杜比实验室特许公司 | Object clustering for rendering object-based audio content based on perceptual criteria |
| WO2016011433A2 (en) * | 2014-07-17 | 2016-01-21 | Origin Wireless, Inc. | Wireless positioning systems |
| KR20190037025A (en) * | 2017-09-28 | 2019-04-05 | 서울대학교산학협력단 | Method for extracting signal unit features using variational inference model based deep learning and system thereof |
| EP3739356A1 (en) * | 2019-05-12 | 2020-11-18 | Origin Wireless, Inc. | Method, apparatus, and system for wireless tracking, scanning and monitoring |
| CN112668652A (en) * | 2020-12-31 | 2021-04-16 | 哈尔滨工业大学 | Method and system for identifying cluster array and motion trend in unmanned equipment confrontation |
| CN112348006A (en) * | 2021-01-11 | 2021-02-09 | 湖南星空机器人技术有限公司 | Unmanned aerial vehicle signal identification method, system, medium and equipment |
| CN112633415A (en) * | 2021-01-11 | 2021-04-09 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle cluster intelligent task execution method and device based on rule constraint training |
Non-Patent Citations (2)
| Title |
|---|
| Smartphone‐based object recognition with embedded machine learning intelligence for unmanned aerial vehicles;Ignacio Martinez‐Alpiste et al.;《Wiley Periodicals》;20191020;全文 * |
| 一种基于RUnet卷积神经网络的地震资料随机噪声压制方法;罗仁泽 等;《石油物探》;20200131;第59卷(第1期);全文 * |
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